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Record W1969509772 · doi:10.1504/ijsmm.2008.017192

Avoiding separation: sport partner perspectives on a long-term inter-organisational relationship

2008· article· en· W1969509772 on OpenAlexaff
Ted Alexander, Lucie Thibault, Wendy Frisby

Bibliographic record

VenueInternational Journal of Sport Management and Marketing · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock UniversityUniversity of British Columbia
Fundersnot available
KeywordsLegitimacyAllianceRecreationPublic relationsGeneral partnershipReciprocity (cultural anthropology)BusinessMarketingPsychologySocial psychologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Researchers have identified the stages of inter-organisational relationships (IOR) (i.e., formation, management, and evaluation), however, few have examined how these stages are enacted over time from the partners' perspectives. We conducted a case study of a dyadic IOR between a non-profit provincial sport organisation (Tennis PSO) and a public sector sport and recreation department over three years. Our analysis revealed that partner motives for forming the alliance differed as one partner focused on determinants of necessity, efficiency, and reciprocity, while the other partner sought increased legitimacy (Oliver, 1990). Both partners were satisfied with the management approach because expertise and resources were shared, clear lines of responsibility and communication were established, and power struggles were avoided. Achieving a mutually desired outcome early helped sustained the IOR, even though only marginal increases in tennis participation occurred. Links between the stages of IOR and the implications of the findings are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.344
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2008
Admission routes1
Has abstractyes

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